4 papers
Act in Collusion: Distributed Multi-Target Backdoor Attacks in Federated Learning
Tao Liu, Dapeng Man, Jiguang Lv +6
Federated learning (FL) is widely used in Internet-of-Things (IoT) systems, but its distributed training process also exposes it to backdoor attacks. Existing studies mainly consid…
FedIDM: Achieving Fast and Stable Convergence in Byzantine Federated Learning through Iterative Distribution Matching
He Yang, Dongyi Lv, Wei Xi +3
Most existing Byzantine-robust federated learning (FL) methods suffer from slow and unstable convergence. Moreover, when handling a substantial proportion of colluded malicious cli…
SNEAKDOOR: Stealthy Backdoor Attacks against Distribution Matching-based Dataset Condensation
He Yang, Dongyi Lv, Song Ma +2
Dataset condensation aims to synthesize compact yet informative datasets that retain the training efficacy of full-scale data, offering substantial gains in efficiency. Recent stud…
PoiCGAN: A Targeted Poisoning Based on Feature-Label Joint Perturbation in Federated Learning
Tao Liu, Jiguang Lv, Dapeng Man +7
Federated Learning (FL), as a popular distributed learning paradigm, has shown outstanding performance in improving computational efficiency and protecting data privacy, and is wid…